{
  "abstract": "Combination therapies are central to cancer treatment, but exhaustive screening is impractical. We introduce PHAROS, a framework that turns a pretrained single-cell perturbation model into a target-directed search engine for drug combinations. PHAROS predicts how a cell population changes under a drug, one drug at a time, then chains these predictions together to simulate drug combinations. It scores each simulated outcome against the desired target state and uses a search algorithm to find the most promising combinations, all without retraining the underlying model. Across two independent combinatorial perturbation datasets, PHAROS recovered exact or mechanism-matched two-drug responses in cell lines, both seen and unseen during model training. Its rankings were specific to the requested conversion and were not explained by single-drug effects, additive effects, or shared mechanism of action. In exploratory analyses of patient-derived metastatic HR+/HER2$-$ breast tumors and basal cell carcinoma (BCC), PHAROS prioritized FDA-approved regimens, distinguished combinations by their predicted tumor-versus-immune objective profiles, and nominated pathway-level hypotheses, while explicitly identifying both tumor cohorts as outside the model's supported distribution. PHAROS provides a modular route from pretrained virtual-cell models to inverse, single-cell combination-screening platforms.",
  "abstract_shown": true,
  "affiliations": [
    "Stanford University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. Bezney"
    },
    {
      "name": "C. Ruggeri"
    },
    {
      "name": "F. Borra"
    },
    {
      "name": "L. S. Qi"
    },
    {
      "name": "F. M. Buffa"
    },
    {
      "name": "L. M. Steinmetz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c4zrb94qh8p03.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.749477",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749477v1.full",
  "id": "c4zrb94qh8p03",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749477v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7212153848",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749477v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c4zrb94qh8p03.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:09Z",
      "native_id": "10.64898/2026.09.08.749477",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749477v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "PHAROS: turning single-cell perturbation models into target-directed drug-combination screens",
  "title_text": "PHAROS: turning single-cell perturbation models into target-directed drug-combination screens",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:09Z",
  "url": "https://research.draconitic.com/paper/c4zrb94qh8p03",
  "venue": "bioRxiv",
  "versions": []
}